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Questions tagged [multilayer-perceptrons]

For question about Multi Layer Perceptron model/architecture, its training and other related details and parameters associated with the model.

A multi-layer perceptron (MLP) is a class of feed-forward artificial neural network. An MLP consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called back-propagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.

Source: Wikipedia - Multilayer perceptron

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What is the number of neurons required to approximate a polynomial of degree n?

I learned about the universal approximation theorem from this guide. It states that a network even with a single hidden layer can approximate any function within some bound, given a sufficient number of neurons. Or mathematically, ${|g(x)−f(x)|<…
mark mark
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Did Minsky and Papert know that multi-layer perceptrons could solve XOR?

In their famous book entitled Perceptrons: An Introduction to Computational Geometry, Minsky and Papert show that a perceptron can't solve the XOR problem. This contributed to the first AI winter, resulting in funding cuts for neural networks.…
rcpinto
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Why use a recurrent neural network over a feedforward neural network for sequence prediction?

If recurrent neural networks (RNNs) are used to capture prior information, couldn't the same thing be achieved by a feedforward neural network (FFNN) or multi-layer perceptron (MLP) where the inputs are ordered sequentially? Here's an example I saw…
SuperCodeBrah
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Why do feedforward neural networks require the inputs to be of a fixed size, while RNNs can process variable-size inputs?

Why does a vanilla feedforward neural network only accept a fixed input size, while RNNs are capable of taking a series of inputs with no predetermined limit on the size? Can anyone elaborate on this with an example?
Daniel
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What are some datasets to train an MLP on simple tasks?

I have implemented an MLP. Now, I want to train it to solve simple tasks. Are there any data sets to train the MLP on simple tasks, that is, tasks with a small number of inputs and outputs? I would like to train it to solve problems which are…
David Price
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Can neurons in MLP and filters in CNN be compared?

I know they are not the same in working, but an input layer sends the input to $n$ neurons with a set of weights, based on these weights and the activation layer, it produces an output that can be fed to the next layer. Aren't the filters the same,…
Tibo Geysen
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Is a multilayer perceptron a recursive function?

I read somewhere that a multilayer perceptron is a recursive function in its forward propagation phase. I am not sure, what is the recursive part? For me, I would see an MLP as a chained function. So, it would nice anyone could relate an MLP to a…
user3352632
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How to deal with padded inputs in a fully connected feed forward network?

I have a fully connected network that takes in a variable-length input padded with 0. However, the network doesn't seem to be learning and I am guessing that the high number of zeros in the input might have something to do with that. Are there…
silkAdmin
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How does a single hidden layer affect output?

I'm learning about multilayer perceptrons, and I have a quick theory question in regards to hidden layer neurons. I know we can use two hidden layers to solve a non-linearly separable problem by allowing for a representation with two linear…
Howard P
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Is it a valid assumption that a purely MLP based tic-tac-toe player will learn lookahead strategies?

I'm doing a little tic-tac-toe project to learn neural networks and machine learning (beginner level). I've written a MLP based program that plays with other search based programs and trains with the data generated from the games. The training and…
Achilles
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Why is it called back-propagation?

While looking at the mathematics of the back-propagation algorithm for a multi-layer perceptron, I noticed that in order to find the partial derivative of the cost function with respect to a weight (say $w$) from any of the hidden layers, we're just…
Skawang
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Why MLP cannot approximate a closed shape function?

[TL;DR] I generated two classes Red and Blue on a 2D space. Red are points on Unit Circle and Blue are points on a Circle Ring with radius limits (3,4). I tried to train a Multi Layer Perceptron with different number of hidden layers, BUT all the…
entropyfeverone
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Are the labels updated during training in the algorithm presented in "An algorithm for correcting mislabeled data"?

I am trying to understand an algorithm for correcting mislabeled data in the paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al. The authors are suggesting to update the output class probability vector using the formula…
ViB
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Is it expected that adding an additional hidden layer to my 3-layer ANN reduces accuracy significantly?

I've been using several resources to implement my own artificial neural network package in C++. Among some of the resources I've been using are…
Chris
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Backpropagation equation for a variant on the usual Linear Neuron architecture

Recently I encountered a variant on the normal linear neural layer architecture: Instead of $Z = XW + B$, we now have $Z = (X-A)W + B$. So we have a 'pre-bias' $A$ that affects the activation of the last layer, before multiplication by weights. I…
Laksh
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